class_docstr: Augmenter that simulate ocr error by random values. For example, OCR may recognize I as 1 incorrectly.        Pre-defined OCR mapping is leveraged to replace character by possible OCR error.
  
  :param float aug_char_p: Percentage of character (per token) will be augmented.
  :param int aug_char_min: Minimum number of character will be augmented.
  :param int aug_char_max: Maximum number of character will be augmented. If None is passed, number of augmentation is
      calculated via aup_char_p. If calculated result from aug_char_p is smaller than aug_char_max, will use calculated result
      from aup_char_p. Otherwise, using aug_max.
  :param float aug_word_p: Percentage of word will be augmented.
  :param int aug_word_min: Minimum number of word will be augmented.
  :param int aug_word_max: Maximum number of word will be augmented. If None is passed, number of augmentation is
      calculated via aup_word_p. If calculated result from aug_word_p is smaller than aug_word_max, will use calculated result
      from aug_word_p. Otherwise, using aug_max.
  :param int min_char: If word less than this value, do not draw word for augmentation
  :param list stopwords: List of words which will be skipped from augment operation.
  :param str stopwords_regex: Regular expression for matching words which will be skipped from augment operation.
  :param func tokenizer: Customize tokenization process
  :param func reverse_tokenizer: Customize reverse of tokenization process
  :param obj dict_of_path: Use pre-defined dictionary by default. Pass either file path of dict to use custom mapping. 
  :param str name: Name of this augmenter
  
  >>> import nlpaug.augmenter.char as nac
  >>> aug = nac.OcrAug()
code: def skip_aug(self, token_idxes, tokens):
      results = []
      for token_idx in token_idxes:
          # Some character mapping do not exist. It will be excluded in lucky draw.
          char = tokens[token_idx]
          if (
              char in self.model.model
              and len(self.model.predict(char)) > 0
          ):
              results.append(token_idx)
  
      return results
  
file_path: nlpaug/nlpaug/augmenter/char/ocr.py
imports: import os
  from nlpaug.augmenter.char import CharAugmenter
  from nlpaug.util import Action, Method, Doc, ReadUtil, LibraryUtil
  import nlpaug.model.char
label_desc: Use image node_id 2 for calling the OcrAug obj's underlying member method code with example usage: obj.skip_aug(token_idxes, tokens) and returns: results